# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Adapted from: https://github.com/MIC-DKFZ/batchgenerators (commit id: 01f225d)
#
# Copyright 2021 Division of Medical Image Computing, German Cancer Research Center (DKFZ)
# and Applied Computer Vision Lab, Helmholtz Imaging Platform
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np

from cerebras.modelzoo.data.vision.segmentation.transforms.resample_augmentations import (
    augment_linear_downsampling_scipy,
)


class SimulateLowResolutionTransform:
    """Downsamples each sample (linearly) by a random factor and upsamples to original resolution again
    (nearest neighbor)

    Info:
    * Uses scipy zoom for resampling.
    * Resamples all dimensions (channels, x, y, z) with same downsampling factor (like isotropic=True from
    linear_downsampling_generator_nilearn)

    Args:
        zoom_range: can be either tuple/list/np.ndarray or tuple of tuple. If tuple/list/np.ndarray, then the zoom
        factor will be sampled from zoom_range[0], zoom_range[1] (zoom < 0 = downsampling!). If tuple of tuple then
        each inner tuple will give a sampling interval for each axis (allows for different range of zoom values for
        each axis

        p_per_channel:

        per_channel (bool): whether to draw a new zoom_factor for each channel or keep one for all channels

        channels (list, tuple): if None then all channels can be augmented. If list then only the channel indices can
        be augmented (but may not always be depending on p_per_channel)

        order_downsample:

        order_upsample:
    """

    def __init__(
        self,
        zoom_range=(0.5, 1),
        per_channel=False,
        p_per_channel=1,
        channels=None,
        order_downsample=1,
        order_upsample=0,
        data_key="data",
        p_per_sample=1,
        ignore_axes=None,
    ):
        self.order_upsample = order_upsample
        self.order_downsample = order_downsample
        self.channels = channels
        self.per_channel = per_channel
        self.p_per_channel = p_per_channel
        self.p_per_sample = p_per_sample
        self.data_key = data_key
        self.zoom_range = zoom_range
        self.ignore_axes = ignore_axes

    def __call__(self, **data_dict):
        for b in range(len(data_dict[self.data_key])):
            if np.random.uniform() < self.p_per_sample:
                data_dict[self.data_key][b] = augment_linear_downsampling_scipy(
                    data_dict[self.data_key][b],
                    zoom_range=self.zoom_range,
                    per_channel=self.per_channel,
                    p_per_channel=self.p_per_channel,
                    channels=self.channels,
                    order_downsample=self.order_downsample,
                    order_upsample=self.order_upsample,
                    ignore_axes=self.ignore_axes,
                )
        return data_dict
